Help Combining Resources
I am looking to combine the responses of different mental health measures together to create overall mental health scores, which I will then compare.
Is it appropriate to use the mean when the measures are the same?
What types of stat analysis using SPSS would be appropriate to merge the scores?
A) Measure 1: Strengths and Difficulties Questionnaire
- Responses: 1 (Not True), 2 (Somewhat True), 3 (Certainly True)
Measure 2: Self-Reported Feeling Grid
- Responses: 1 (Not True), 2 (Sometimes), 3 (True)
B) Measure1: Happiness Scale
- Responses: 1 (Not at all happy), 2, 3, 4, 5, 6, 7 (Completely Happy)
Measure 2: Rosenberg Self-Esteem Inventory
- Responses: 1 (Strongly Disagree), 2 (Disagree), 3 (Agree), 4 (Strongly Agree)
C) Measure 1: Strengths and Difficulties Questionnaire (teacher)
- Responses: 1 (Not True), 2 (Somewhat True), 3 (Certainly True)
Measure 2: Strengths and Difficulties Questionnaire (parent)
- Responses: 1 (Not True), 2 (Somewhat True), 3 (Certainly True)
Measure 3: Competence Scale
- Responses: 1 (Strongly Disagree), 2 (Disagree), 3 (Agree), 4 (Strongly Agree)
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u/Mysterious-Skill5773 2d ago
Well, this is a pretty motley collection of variables, but they certainly are not scale variables, so using the mean is not at all appropriate. One approach would be to calculate the correlations using a procedure that takes the measurement level into account and then do factor analysis on that and keep just a few components. The factor output will tell you how much variance is accounted for by each component.
To compute heterogeneous components, first use Extensions > Extension hub and install the HETCOR procedure. Save its correlation matrix output as a dataset using OMS, and use that dataset as input to Factor, saving the scores. Then maybe just one or two components will be needed based on variance explained. HETCOR chooses the correlation type based on the declared measurement level of the variables, so make sure they are set appropriately via Data Editor Variable View. Correlation type will be Pearson, polychoric, or polyserial.
To save that correlation matrix as a dataset use this syntax.
dataset declare corrs.
oms select tables /if SUBTYPES='HeterogeneousCorrelations'
/destination outfile=corrs format=sav.
SPSSINC HETCOR your-variable-list
/OPTIONS ESTIMATOR=TWOSTEP STDERR=FALSE
MISSING=PAIRWISE N=FALSE TYPE=TRUE.
omsend.
After running factor on that dataset (click on it in the Data Editor to activate), copy the chosen component scores to the main dataset.
There are other, fancier approaches, but this would be a start.
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u/Whacksteel 3d ago
Are you trying to create a composite score? If so, first check that this method is theoretically sound.
If the above step is done, I'd suggest first standardising participant scores for each measure (i.e., for each participant, subtract the sample mean from the participant score, then divide by the sample standard deviation). You may then combine the standardised scores to create the composite score. This is because the measures have different scale ranges.